Phase Detection and Modulation Improvement for Active Load Modulation during Continuous Transmission

The paper covers one of the communication technologies used in wireless sensor networks. We have presented improvements in existing radio frequency identification (RFID) systems to address the problem of the phase selection in active load modulation (ALM). The phase selection affects the interoperability of communication devices and has to be addressed in the design phase of a new tag. A novel transmission method is presented to make the phase selection irrelevant for device interoperability. A second solution is shown to improve the existing system synchronization, which allows operation with arbitrary selected phase. A mathematical analysis of signals present on the antenna was used together with the reference reader model to perform an analysis of proposed improvements. We proved that the proposed transmission method is less affected by phase selection. Furthermore, we demonstrated that existing system improvement allows synchronization and operation at an arbitrarily selected phase despite the continuous transmission and large signal-to-interference ratio.

Efficient Interference Estimation with Accuracy Control for Data-Driven Resource Allocation in Cloud-RAN

The emerging edge computing paradigm has given rise to a new promising mobile network architecture, which can address a number of challenges that the operators are facing while trying to support growing end user's needs by shifting the computation from the base station to the edge cloud computing facilities. With such powerfully computational power, traditional unpractical resource allocation algorithms could be feasible. However, even with near optimal algorithms, the allocation result could still be far from optimal due to the inaccurate modeling of interference among sensor nodes. Such a dilemma calls for a measurement data-driven resource allocation to improve the total capacity. Meanwhile, the measurement process of inter-nodes' interference could be tedious, time-consuming and have low accuracy, which further compromise the benefits brought by the edge computing paradigm. To this end, we propose a measurement-based estimation solution to obtain the interference efficiently and intelligently by dynamically controlling the measurement and estimation through an accuracy-driven model. Basically, the measurement cost is reduced through the link similarity model and the channel derivation model. Compared to the exhausting measurement method, it can significantly reduce the time cost to the linear order of the network size with guaranteed accuracy through measurement scheduling and the accuracy control process, which could also balance the tradeoff between accuracy and measurement overhead. Extensive experiments based on real data traces are conducted to show the efficiency of the proposed solutions.